Search

Introduction: Patient education is recognized to be essential in orthotic and prosthetic care. However, there are no current standardized methods of educating patients. This literature review looked at the most effective current methods of educating patients in terms of comprehension, recall, and compliance. Methods: Inclusion and exclusion criteria for articles were determined. Search strategy was developed using keywords, MeSH terms, and Boolean operators and applied to three different databases (Medline Ovid, Embase, and Web of Science). Results: After searching on Medline Ovid, Embase, and Web of Science, 833 articles were found after deduplication, with 49 articles included in the final review. Conclusions: The review found that delivering the education in terms of behavioral advice and multimodal methods was the most effective. Patients' preference of education delivery, mood, attitude toward their condition, and relationship with their provider also need to be considered. Further research needs to be done on effective methods of delivering patient education. Clinical Relevance: This research has the potential to lead to future studies in educating patients who utilize orthotic and prosthetic devices. The aim of the project is to eventually standardize or create best practice guidelines for patient education in the field of Orthotics and Prosthetics with the intent of improving comprehension, recall, and compliance. [ABSTRACT FROM AUTHOR]

Background: Proper medication reconciliation (= comparing the accuracy of patient-reported medication use with pharmacy records) could prevent potentially dangerous situations such as drug–drug interactions and hospitalization. This is particularly important when patients rely on multiple medications, such as in neurodegenerative disorders like Huntington’s Disease (HD). Currently, it is unknown how often medication discrepancies occur in HD patients and which factors contribute to the discrepancies. Objective: Identify prognostic factors of medication discrepancies in HD, using patient-reported medication use and local pharmacy records. Methods: With 134 pre- and manifest HD patients, we performed a multivariable logistic regression analysis with medication discrepancy as dependent variable (pharmacy records as reference value) and sex, CAP score, disease status (pre- or manifest HD), number of concomitant medications taken, presence of an informal caregiver, Unified Huntington’s Disease Rating Scale-Total Functioning Capacity, unified cognitive Z-score and Problem Behaviors Assessment-short characteristic scores for depression, anxiety, and apathy as independent variables. Results: Medication discrepancies were reported frequently, both in premanifest (43.2%) and manifest HD subjects (36.7%). Impaired cognition significantly predicted medication discrepancies (beta = −0.688, SE 0.27, p = 0.011). All other variables were non-significant. Conclusions: Regardless of HD disease status and stage, patient self-reported medication use is not a reliable source, especially in those with impaired cognitive function. The presence of an informal caregiver and the absence of polypharmacy, depression, anxiety and apathy do not influence self-reported medication accuracy. Objective verification of medication use with HD patients’ local pharmacy is recommended. Trial registration number: ICTRP-NL-OMON55123, HD-med, registered 26-Nov-2019. [ABSTRACT FROM AUTHOR]

Over the years, there has been an increase in the number of people sent to prison for a sexual offence, yet there is a lack of research exploring the experiences of those with sexual convictions within the prison system, and the factors that help or hinder their progression towards release. This research aimed to explore the experiences of individuals with sexual convictions who have progressed to an open prison but have been recalled back to a closed prison. The research took a qualitative approach, undertaking semi-structured interviews with 10 individuals who had moved back to a closed prison. Thematic analysis was used, eliciting two main themes. First, failure was the only option, relays how participants felt they were bound to fail at open conditions, largely due to a lack of information which meant they did not know what to expect, and a lack of support upon arrival. They also felt stigmatised because of their convictions. A different world centres around participants reporting entering into an unfamiliar environment in open conditions, leaving them unsettled. It also describes the difficulties participants had adjusting to the freedom of open conditions but also the delays they experienced. Implications for practice and future research are discussed. [ABSTRACT FROM AUTHOR]

Introduction: Implicit statistical learning is, by definition, learning that occurs without conscious awareness. However, measures that putatively assess implicit statistical learning often require explicit reflection, for example, deciding if a sequence is 'grammatical' or 'ungrammatical'. By contrast, 'processing-based' tasks can measure learning without requiring conscious reflection, by measuring processes that are facilitated by implicit statistical learning. For example, when multiple stimuli consistently co-occur, it is efficient to 'chunk' them into a single cognitive unit, thus reducing working memory demands. Previous research has shown that when sequences of phonemes can be chunked into 'words', participants are better able to recall these sequences than random ones. Here, in two experiments, we investigated whether serial visual recall could be used to effectively measure the learning of a more complex artificial grammar that is designed to emulate the between-word relationships found in language. Methods: We adapted the design of a previous Artificial Grammar Learning (AGL) study to use a visual serial recall task, as well as more traditional reflection-based grammaticality judgement and sequence completion tasks. After exposure to "grammatical" sequences of visual symbols generated by the artificial grammar, the participants were presented with novel testing sequences. After a brief pause, participants were asked to recall the sequence by clicking on the visual symbols on the screen in order. Results: In both experiments, we found no evidence of artificial grammar learning in the Visual Serial Recall task. However, we did replicate previously reported learning effects in the reflection-based measures. Discussion: In light of the success of serial recall tasks in previous experiments, we discuss several methodological factors that influence the extent to which implicit statistical learning can be measured using these tasks. [ABSTRACT FROM AUTHOR]

Background: In systematic reviews (SRs), geographical limitations in literature searches can aid in focussing research efforts. A methodological review published in 2016 examined the approaches SR authors use to identify studies about Germany, analysing 36 SRs. Objective: The aim of this study was to update the original review. Methods: We conducted a literature search on PubMed for SRs synthesising evidence from studies about Germany published between 22 January 2016 and 7 June 2022. Two reviewers independently performed study selection and data extraction. We evaluated the application of search syntax for restricting studies to those about Germany using the peer review of electronic search strategies criteria. The updated findings were reported and summarised alongside those of the original review. Results: Thirty‐two additional SRs were newly included (total = 68). Geographic restrictions were applied in 57 SRs, representing 72% in the original review and increasing to 97% in the newly included SRs. Moreover, there was an increased use of truncations and field tags. Conclusion: Although geographical restriction methods are increasingly utilised, additional tools are necessary to enhance the robustness of search strategies. The development of a dedicated geographical search filter would facilitate the identification of studies about Germany. [ABSTRACT FROM AUTHOR]

Introduction: Research shows that stimuli rated for their relevance to a survival scenario have a higher recall probability when compared to other deep encoding conditions. This phenomenon, known as survival memory advantage, is a robust finding that has been demonstrated by various experimental manipulations. The current study aimed to examine associations between participants' psychological markers (perceived stress, anxiety, coping, cognitive control, and flexibility) and their memory performance in survival and other encoding conditions (fight, flight, and pleasantness). Methods: A total of 141 undergraduates aged 18--35 years participated voluntarily in the study. Four scenario situations (fight, flight, survival, or pleasantness) were randomly allocated to participants, and then they were given a list of words to rate for their relevance to the scenarios. Participants were given a free recall task to measure their memory, while some psychological scales (perceived stress, anxiety level, coping strategies, cognitive control, and flexibility) were administered to assess their psychological markers. Results: Survival conditions yielded the highest correct recall. Pairwise comparisons showed that difference between survival and pleasant conditions was significant (p<0.05). The other conditions did not differ significantly from one another. Regression analyses revealed that anxiety level may explain 13% of variance in survival condition and 14% of variance in fight condition. No significant effect was found on flight conditions. Conclusion: Recall performance did not significantly differ between survival, fight, and flight conditions. However, anxiety level in survival conditions and support seeking in fight condition were found to be negative predictors of recollection. According to these results, associations between concepts of anxiety and survival, and between support seeking and fight (struggle) in human mind determine memory processes at a significant level. Individuals' psychological characteristics and coping strategies have different effects on recall depending on the context. [ABSTRACT FROM AUTHOR]

Purpose: We evaluated whether recalling personal achievements would result in an increase in feelings of pride, and if so, whether social anxiety (SA) moderated this relationship. Methods: Community samples were recruited in 2022 via the online platform Prolific. On average, participants in both studies were aged 32–33, had some post-secondary education, were married/cohabitating and self-identified as White (60%), Asian (10%) or Black (7%). Participants completed measures of state pride and affect before and after different types of writing tasks. Study 1 participants (N = 398) recalled and wrote about either one or three personal achievement experiences. To control for the general effects of self-reflection, Study 2 participants (N = 396) wrote about either achievement or non-achievement-oriented events. Results: Mixed-model Time X Condition ANCOVAs were conducted with pride as the dependent variable and social anxiety as covariate. Both studies revealed significant increases in pride and positive affect following recall of achievement events, and (Study 2) no significant change following recall of non-achievement events. SA had no significant effect on change in pride, suggesting that SA did not suppress the benefits of achievement recall. Conclusions: Facilitating recall of personal achievements may help to heighten pride and positive affect regardless of social anxiety level. The results support further research on the role of pride in social anxiety. Highlights: Participants recalled and wrote about personal achievements. Achievement recall significantly increased feelings of pride. Social anxiety did not affect type of achievements recalled. Social anxiety did not impede increases in pride. [ABSTRACT FROM AUTHOR]

Mind wandering occurs when a reader's thoughts are unrelated to the text's ideas. We examined the relation between mind wandering and readers' memory for text. More specifically, we assessed whether mind wandering inhibits the reader's development of the situation model and thus their ability to identify and recall the text's most central ideas. Undergraduate participants (M = 18.92 years; SD = 1.32) read and recalled three expository passages. Participants responded to intermittent probes to report mind wandering frequency. We examined how mind wandering impacted the readers' situation model, indicated by the proportion of central and peripheral ideas recalled. Using path analysis models, we found that mind wandering negatively predicted the recall of central, but not peripheral, ideas. The effect of mind wandering on the recall of central ideas was not explained by working memory span (measured by WAIS-IV digit span backward and letter-number sequencing), word reading skill (measured by Letter-Word Identification and Word Attack), or general reading comprehension skill (measured by the Gates-MacGinitie Reading Test). These results indicate that mind wandering hinders the recognition and recall of a text's most central ideas and suggest that mind wandering impacts the development of a coherent situation model. This effect seems to be independent of working memory, word reading, and general reading comprehension skill. Future studies should test approaches to decrease mind wandering among adult readers. [ABSTRACT FROM AUTHOR]

The purpose of the current study was to expand upon previous research on RECALL, a dialogic reading intervention modified for autistic children aimed at increasing engagement. Children ages 3–6 years (n = 6) with language delays with or without co-occurring autism were tested using a multiple baseline across participants design. During baseline, the interventionist used dialogic reading and asked questions after every page. During intervention, the interventionist used RECALL, including a least to most prompting hierarchy with visual prompt cards. Children were more responsive and produced more meaningful correct responses during the intervention. Response type (linguistic vs. non-linguistic) also changed from baseline to intervention, though the pattern varied across participants. Intervention was not associated with increased responsiveness to adult bids for attention or pauses designed to encourage the child to initiate an interaction, though a few children showed changes in these responses over time. [ABSTRACT FROM AUTHOR]

Visuospatial bootstrapping (VSB) refers to the phenomenon in which performance on a verbal working memory task can be enhanced by presenting the verbal material within a familiar visuospatial configuration. This effect is part of a broader literature concerning how working memory is influenced by use of multimodal codes and contributions from long-term memory. The present study aimed to establish whether the VSB effect extends over a brief (5-s) delay period, and to explore the possible mechanisms operating during retention. The VSB effect, as indicated by a verbal recall advantage for digit sequences presented within a familiar visuospatial configuration (modelled on the T-9 keypad) relative to a single-location display, was observed across four experiments. The presence and size of this effect changed with the type of concurrent task activity applied during the delay. Articulatory suppression (Experiment 1) increased the visuospatial display advantage, while spatial tapping (Experiment 2) and a visuospatial judgment task (Experiment 3) both removed it. Finally, manipulation of the attentional demands placed by a verbal task also reduced (but did not abolish) this effect (Experiment 4). This pattern of findings demonstrates how provision of familiar visuospatial information at encoding can continue to support verbal working memory over time, with varying demands on modality-specific and general processing resources. [ABSTRACT FROM AUTHOR]

Despite bilateral hippocampal damage dating to the perinatal or early childhood period and severely impaired episodic memory, patients with developmental amnesia continue to exhibit well‐developed semantic memory across the developmental trajectory. Detailed information on the extent and focality of brain damage in these patients is needed to hypothesize about the neural substrate that supports their remarkable capacity for encoding and retrieval of semantic memory. In particular, we need to assess whether the residual hippocampal tissue is involved in this preservation, or whether the surrounding cortical areas reorganize to rescue aspects of these critical cognitive memory processes after early injury. We used voxel‐based morphometry (VBM) analysis, automatic (FreeSurfer) and manual segmentation to characterize structural changes in the brain of an exceptionally large cohort of 23 patients with developmental amnesia in comparison with 32 control subjects. Both the VBM and the FreeSurfer analyses revealed severe structural alterations in the hippocampus and thalamus of patients with developmental amnesia. Milder damage was found in the amygdala, caudate, and parahippocampal gyrus. Manual segmentation demonstrated differences in the degree of atrophy of the hippocampal subregions in patients. The level of atrophy in CA‐DG subregions and subicular complex was more than 40%, while the atrophy of the uncus was moderate (−24%). Anatomo‐functional correlations were observed between the volumes of residual hippocampal subregions in patients and selective aspects of their cognitive performance, viz, intelligence, working memory, and verbal and visuospatial recall. Our findings suggest that in patients with developmental amnesia, cognitive processing is compromised as a function of the extent of atrophy in hippocampal subregions. More severe hippocampal damage may be more likely to promote structural and/or functional reorganization in areas connected to the hippocampus. In this hypothesis, different levels of hippocampal function may be rescued following this variable reorganization. Our findings document not only the extent, but also the limits of circuit reorganization occurring in the young brain after early bilateral hippocampal damage. [ABSTRACT FROM AUTHOR]

Objective. This study's primary objective was to examine the risk of recall for novel spine devices over time. Secondarily, we sought to analyze interbody fusion and vertebral body replacement (VBR) devices (corpectomy cages) as a risk factor for recall. Background. The recall risk of a novel spine device over time has not been reported. In addition, FDA regulations were lowered for interbody fusion devices to enter the market in 2007. As well, VBR implants were recently approved by the FDA for use in the cervical spine in 2015. Materials and Methods. Spine devices cleared between January 1, 2008 and December 31, 2018 were identified from the FDA's 510(k) database. All recall data were collected from the database in January 2021 to provide a 2-year minimum follow-up for a recall to occur. Product labels were used to classify interbody fusion and VBR devices. Cumulative incidence function was conducted to compare the overall risk of recall for FDA-cleared spine devices, and the hazard ratio determined for VBR and all other devices versus interbody implants during the study period. Results. A total of 2384 spine devices were cleared through 510 (k) in the study period. The hazard of recall at 5 years was 5.3% (95% CI: 4.4%-6.2%) and 6.5% (95% CI: 5.4%-7.7%) at 10 years. No significant difference in recall risk was identified for interbody fusion and VBR devices. Conclusion. The risk of recall at 5 and 10 years of a novel spine device is about half the 12% rate reported for orthopedic devices in general. Despite lowered FDA regulations for interbody fusion devices and recent approval for VBR device use in the cervical spine, no increased risk of recall was detected. Further research is necessary to explain the reason for the lower risk of recall with spine devices. [ABSTRACT FROM AUTHOR]

This study delves into the exploration of broadband measurement techniques for power systems, utilizing wavelet transform as a foundational tool for signal analysis. The research rigorously evaluates the efficacy of several machine learning algorithms, namely Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest, in interpreting and analyzing broadband signals within power systems. Through a detailed analytical process, the performance of each algorithm is meticulously assessed based on several critical metrics: accuracy, precision, recall, and F1-score. The research investigates broadband measurement methods for power systems using wavelet transform and evaluates the performance of Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest. Results show SVM achieving an accuracy of 85%, precision of 86%, recall of 82%, and F1-score of 84%. ANN yields 82% accuracy, 84% precision, 78% recall, and 81% F1 score. KNN demonstrates 87% accuracy, 88% precision, 84% recall, and 86% F1 score. DT achieves 79% accuracy, 80% precision, 75% recall, and 77% F1 score. Overall, the study provides insights into machine learning algorithms' effectiveness in broadband power system measurement. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To compare the agreement and cost of two recall methods for estimating childrens minimum dietary diversity (MDD). DESIGN: We assessed childs dietary intake on two consecutive days: an observation on day one, followed by two recall methods (list-based recall and multiple-pass recall) administered in random order by different enumerators at two different times on day two. We compared the estimated MDD prevalence using survey-weighted linear probability models following a two one-sided test equivalence testing approach. We also estimated the cost-effectiveness of the two methods. SETTING: Cambodia (Kampong Thom, Siem Reap, Battambang, and Pursat provinces) and Zambia (Chipata, Katete, Lundazi, Nyimba, and Petauke districts). PARTICIPANTS: Children aged 6-23 months: 636 in Cambodia and 608 in Zambia. RESULTS: MDD estimations from both recall methods were equivalent to the observation in Cambodia but not in Zambia. Both methods were equivalent to the observation in capturing most food groups. Both methods were highly sensitive although the multiple-pass method accurately classified a higher proportion of children meeting MDD than the list-based method in both countries. Both methods were highly specific in Cambodia but moderately so in Zambia. Cost-effectiveness was better for the list-based recall method in both countries. CONCLUSION: The two recall methods estimated MDD and most other infant and young child feeding indicators equivalently in Cambodia but not in Zambia, compared to the observation. The list-based method produced slightly more accurate estimates of MDD at the population level, took less time to administer and was less costly to implement.

The world of pathology encompasses a broad spectrum of diseases beyond the boundaries of specialties, with each disease having specific and interesting gross and microscopic features. Some of these features are pattern-based, while others are eponyms that compare them to objects, food, animals, etc., due to their striking resemblance and quick recall. Medical nomenclature is of vital importance, and it has significantly evolved historically, with etymological roots from Latin, Greek, and Roman languages of ancient times to the current internationally uniform codes of English and other modern languages. Eponyms are valuable medical literary epithets that have been used across specialties and include animal names, food names, discoverer names, geographic references, and more. The subject of pathology, in particular, has immense use of eponyms as they are valuable tools for assisting in adult learning, or andragogy. The specialty of pathology is unique in its complex patterns and diagnostic algorithm, always in need of alternate systems to arrive at quick and accurate diagnosis. Also known as intuitive thinking or reflex thinking, pattern viewing and eponyms trigger a reflex recognition system, reducing recall time and aiding in precise diagnosis. The present article aimed to review the terminological phenomenon of animal eponym usage in the context of pathological diagnosis.

Retelling an event in a social setting often means talking about it less factually than we might if trying to recall it as accurately as possible. These distortions that arise from socially oriented retellings could affect the ability to later recall the same event accurately. Does retelling a story in a social situation impair memory compared to not retelling it at all? Or could retrieving the memory, even with a socially oriented mindset, still improve memory? We explored social retelling's effect on memory in a two-session study. Participants heard two stories twice and, after a distractor task, retold the stories according to one of three randomly assigned conditions: social retelling (retell the stories as if talking to friends), accuracy retelling (retell the stories as accurately as possible), or no retelling. A day later, everyone retold the stories as accurately as possible. Participants in the accuracy retelling group included more specific details in their session two retellings than did the social retelling group, which included more specific details than the no retelling group. Elaborations in session two did not differ across groups. Findings suggest retelling a story in a social situation benefits memory, though not as much as retelling a story accurately does. [ABSTRACT FROM AUTHOR]

The world is progressing at a rapid rate, and with continuous advancements in technology, the need for cooperative spectrum sensing has evolved. Cooperative spectrum sensing is an approach used to enhance detection performance, wherein secondary users collaborate with each other to sense the spectrum and identify spectrum holes. As technology improves over time, the spectrum becomes increasingly allocated to primary users. The role of cooperative spectrum sensing in cognitive radio fulfills the essential requirement of protecting primary users from harmful interference. The progressive evolution of technology has led to a reduction in available spectrum, prompting the emergence of the concepts of cognitive radio and cooperative spectrum sensing. To begin with, this paper introduces the fundamental application of cooperative spectrum sensing algorithms, encompassing primary and secondary users' models and an energy model. Subsequently, three machine learning algorithms, namely K-means, support vector machine, and logistic regression, are explained, and their schematics are presented in detail. The proposed model uses supervised and unsupervised learning techniques to develop a cooperative spectrum sensing framework. It compares the performance of three machine learning algorithms, K-means clustering, logistics regression and support vector machine. The comparison is based on accuracy, recall and precision metrics, and the results show that the K-means clustering algorithm has better performance than the other two algorithms. The findings highlight the superiority of the K-means clustering algorithm over logistic regression and support vector machine. [ABSTRACT FROM AUTHOR]

In traditional digital filing systems, people mostly use text as a key to categorise images, and retrieve them in the future. The use of other media as keys for image retrieval is rarely used, notwithstanding that multisensory digital media – mulsemedia – can be harnessed to improve users' performance and help them to retrieve their images. In this respect, olfactory media (engaging the sense of smell) is an example, as people can categorise their images by using congruent olfactory media. Accordingly, we investigated the impact of employing olfactory media as a key for retrieving a set of images. Moreover, we also studied the impact of the usage of olfactory media in this context on a user's performance and Quality of Experience (QoE). To this end, we developed an olfactory-enhanced application (SCENT2IMAGE) in which olfactory media was emitted alongside a 5X5 matrix of images, of which users had to recognize 4 images congruent with the emitted scents. Furthermore, we developed a word-only version of the application (WORD2IMAGE) in which words alone were used as an equivalent key instead of olfactory media. Forty-four participants were invited and took part in our experiment, evenly split into a control and experimental group. Results highlight that using olfactory media does have a significant impact on user performance by helping them find related images. Moreover, using olfactory effects in this context was also found to enhance user QoE. Lastly, our findings underscore that users were willing to use olfactory-enhanced applications for categorizing/retrieving their albums and images. [ABSTRACT FROM AUTHOR]

Introduction: While factors such as age and education have been associated with persistent differences in functional cognitive decline, they do not fully explain observed variations particularly those between different racial/ethnic and sex groups. The aim of this study was to explore the association between allostatic load (AL) and cognition in a racially diverse cohort of young adults. Methods: Utilizing Wave V of the National Longitudinal Study of Adolescent to Adult Health – a nationally representative, longitudinal survey of adults aged 34–44, this study utilized primary data from 10 immune, cardiovascular, and metabolic biomarkers to derive an AL Index. Cognition was previously recorded through word and number recall scores. Regression analysis evaluated the association between cognitive recall, AL, age, sex, and race/ethnicity. Results: Regression results indicated statistically higher AL scores among Blacks (IRR = 1.09, CI = 1.01, 1.19) compared to Whites and lower AL score among females compared to males (IRR = 0.76, CI = 0.72, 0.81). At zero AL, Blacks (IRR = 1.2399, CI = 1.2398, 1.24) and Other races (IRR = 1.4523, CI = 1.452, 1.4525) had higher recall while Hispanics (IRR = 0.808, CI = 0.8079, 0.8081) had lower recall compared to Whites. Relative to males, females had higher number recall (IRR = 1.1976, CI = 1.1976, 1.1977). However, at higher, positive levels of AL, Blacks (IRR = 0.9554, CI = 0.9553, 0.9554), Other races (IRR = 0.9479, CI = 0.9479, 0.9479) and females (IRR = 0.9655, CI = 0.9655, 0.9655) had significantly lower number recall than Whites and males respectively. Conclusions: Race and sex differences were observed in recall at different levels of AL. Findings demonstrate the need for further exploration of cognition in young adults across diverse populations that includes examination of AL. [ABSTRACT FROM AUTHOR]

How does our environment impact what we will later remember? Early work in real-world environments suggested that having matching encoding/retrieval contexts improves memory. However, some laboratory-based studies have not replicated this advantageous context-dependent memory effect. Using virtual reality methods, we find support for context-dependent memory effects and examine an influence of memory schema and dynamic environments. Participants (N = 240) remembered more objects when in the same virtual environment (context) as during encoding. This traded-off with falsely "recognizing" more similar lures. Experimentally manipulating the virtual objects and environments revealed that a congruent object/environment schema aids recall (but not recognition), though a dynamic background does not. These findings further our understanding of when and how context affects our memory through a naturalistic approach to studying such effects. [ABSTRACT FROM AUTHOR]

This research delves into the realm of Maritime Cybersecurity, focusing on the application of Artificial Intelligence (AI) mechanisms, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Networks (ANN). The maritime industry faces evolving cyber threats, necessitating innovative approaches for robust defense. The maritime sector is increasingly reliant on digital technologies, making it susceptible to cyber threats. Traditional security measures are insufficient against sophisticated attacks, necessitating the integration of AI mechanisms. This research aims to evaluate the effectiveness of KNN, RF, and ANN in fortifying maritime cybersecurity, providing a proactive defense against emerging threats. Investigate the application of KNN, RF, and ANN in the maritime cybersecurity landscape. Assess the performance of these AI mechanisms in detecting and mitigating cyber threats. Explore the adaptability of KNN, RF, and ANN to the dynamic maritime environment. Provide insights into the strengths and limitations of each algorithm for maritime cybersecurity. The study employs these AI algorithms to analyze historical maritime cybersecurity data, evaluating their accuracy, precision, and recall in threat detection. Results demonstrate the effectiveness of KNN in identifying localized anomalies, RF in handling complex threat landscapes, and ANN in learning intricate patterns within maritime cybersecurity data. Comparative analyses reveal the strengths and weaknesses of each algorithm, offering valuable insights for implementation. In conclusion, the integration of KNN, RF, and ANN mechanisms presents a promising avenue for enhancing maritime cybersecurity. The study underscores the importance of adopting AI solutions to the maritime domain's unique challenges. While each algorithm demonstrates efficacy in specific scenarios, a hybrid approach may offer a comprehensive defense strategy. As the maritime industry continues to evolve, leveraging AI mechanisms becomes imperative for staying ahead of cyber threats and safeguarding critical assets. This research contributes to the ongoing discourse on maritime cybersecurity, providing a foundation for future developments in the field. [ABSTRACT FROM AUTHOR]

Artificial Intelligence-based system for Tuberculosis (TB) detection has been proposed in this work. Manual diagnostic through radiologists can be misdiagnosis due to human error. Designing an artificial intelligence-based decision support system can help in the accurate prediction of TB through lung Chest X-ray (CXR) image. In the proposed work, data centric and model centric approaches are applied. In data centric approach, images play an important role in feature extraction. In this context, experimentation on six varied kinds of image enhancement applied to the publicly available TB image dataset along with data pre-processing and data augmentation. In model centric approach, three pre-trained Convolutional Neural Network (CNN) with modification with specialized layers are proposed. Comparative Study of six image enhancement techniques along with original dataset in behavior response with network architectures is evaluated for best performance. Evaluation of the networks is based on accuracy, precision, recall and AUC. Sharpening of images applied on Modified ResNet50 is the best performer with accuracy 99.05%, precision 98.87%, recall 100% and AUC of 99.29%. In comparability with original dataset, sharpened images performed 0.8% better in metric evaluation, which gives better classification approach and predictability. [ABSTRACT FROM AUTHOR]

Purpose: Despite stringent regulations enforced by the United States Food and Drug Administration (USFDA), numerous drug products still enter the market without adequate assurance of safety and efficacy, resulting in frequent recalls. To explore the real picture of such recalls, this analysis focuses on the USFDA drug recalls with a glance at medical devices, food products, biological products, cosmetics, tobacco products, and veterinary products from 2019 to 2023. The research aims to quantify and categorize these drug recalls, examining various contributing factors to recommend strategies for minimizing future recalls. Additionally, it investigates the influence of global and national crises, such as the COVID-19 pandemic, on the USFDA’s drug recall process. Methods: Data from USFDA Enforcement reports were collected and sorted, and descriptive statistics were used to analyse drug recalls. Results: Between 2019 and 2023, the USFDA documented 31125 recalls, of which 6217 involved drug products from 593 different companies. These recalls are frequently initiated by the companies themselves, with primary causes including sterility assurance failures, contamination, and improper storage conditions. Conclusions: Drug recalls are a double-edged sword: while they are essential for protecting public health, they can also disrupt supply chains and lead to drug shortages, especially when there are no alternatives available. To prevent such disruptions, strict adherence to current Good Manufacturing Practices, along with comprehensive internal audits and rigorous USFDA inspections are vital for manufacturers to maintain quality standards and minimize the risk of recalls. [ABSTRACT FROM AUTHOR]

The world of pathology encompasses a broad spectrum of diseases beyond the boundaries of specialties, with each disease having specific and interesting gross and microscopic features. Some of these features are pattern-based, while others are eponyms that compare them to objects, food, animals, etc., due to their striking resemblance and quick recall. Medical nomenclature is of vital importance, and it has significantly evolved historically, with etymological roots from Latin, Greek, and Roman languages of ancient times to the current internationally uniform codes of English and other modern languages. Eponyms are valuable medical literary epithets that have been used across specialties and include animal names, food names, discoverer names, geographic references, and more. The subject of pathology, in particular, has immense use of eponyms as they are valuable tools for assisting in adult learning, or andragogy. The specialty of pathology is unique in its complex patterns and diagnostic algorithm, always in need of alternate systems to arrive at quick and accurate diagnosis. Also known as intuitive thinking or reflex thinking, pattern viewing and eponyms trigger a reflex recognition system, reducing recall time and aiding in precise diagnosis. The present article aimed to review the terminological phenomenon of animal eponym usage in the context of pathological diagnosis. [ABSTRACT FROM AUTHOR]

We extend the prophet inequality problem to a competitive setting. At every period, a new realization of a random variable with a known distribution arrives, which is publicly observed. Then, two players simultaneously decide whether to pick an available value or to pass and wait until the next period (ties are broken uniformly at random). As soon as a player gets a value, he leaves the market and his payoff is the value of this realization. In the first variant, namely the "no recall" case, the agents can only bid at each period for the current value. In a second variant, the "full recall" case, the agents can also bid for any of the previous realizations which has not been already selected. For each variant, we study the subgame-perfect Nash equilibrium payoffs of the corresponding game. More specifically, we give a full characterization in the full recall case and show in particular that the expected payoffs of the players at any equilibrium are always equal, whereas in the no recall case the set of equilibrium payoffs typically has full dimension. Regarding the welfare at equilibrium, surprisingly the best equilibrium payoff a player can have may be strictly higher in the no recall case. However, the sum of equilibrium payoffs is weakly larger when the players have full recall. Finally, we show that in the case of 2 arrivals and arbitrary distributions, the prices of Anarchy and Stability in the no recall case are at most 4/3, and this bound is tight. [ABSTRACT FROM AUTHOR]

Potato crop has become integral part of our diet due to its wide use in variety of dishes, making it an important food crop. Its importance also stems from the fact that it is one of the cheapest vegetables available throughout the year. This makes it crucial to keep potato prices affordable for developing countries where the majority of the population falls under the middle-income bracket. Consequently, there is a need to develop a robust, effective, and portable technique to detect diseases in potato plant leaves. In this work, an attention-based disease detection technique is proposed. This technique selectively focuses on specific parts of an image which reveal the disease. This technique leverages transfer learning combined with two attention modules: the channel attention module and spatial attention module. By focusing on specific parts of the images, the proposed technique is able to achieve almost similar accuracy with significantly fewer parameters. The proposed technique has been validated using four pre-trained models: DenseNet169, XceptionNet, MobileNet, and VGG16. All of these models are able to achieve almost the same level of training and validation accuracy, around 90–97%, even after reducing the number of parameters by 40–50%. It shows that the proposed technique effectively reduces model complexity without compromising performance. [ABSTRACT FROM AUTHOR]

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Cognitive ability is increasingly recognized as a significant factor influencing household portfolio decisions. However, different cognitive abilities, such as numeracy, fluency and recall, may yield different investment results. The aim of this paper is to empirically examine the associations of three cognitive abilities (numeracy, fluency and recall) with household portfolio composition using Survey of Health, Aging and Retirement (SHARE) data across 16 European countries and the multinomial logit model. Our empirical analyses focus on the impacts of differences in country characteristics, specifically the level of economic development and the existence of a national health system (NHS). The results indicate that numeracy and fluency have positive impacts on the decision to hold safe and relatively risky assets, as well as fully diversified portfolios in developed countries, but have no significant effects in emerging countries. Additionally, all three cognitive abilities positively influence the decision to hold fully diversified portfolios in the countries with NHS, while no significant effects are observed in the countries without NHS. Our findings reveal a decreased impact of cognitive abilities on portfolio types in the emerging countries and the non-NHS countries. Notably, a significant and positive correlation is found between the holding of no financial assets in both non-NHS countries and advanced countries. One important implication of this study is that marketing strategies of financial advisors should take into account household cognitive abilities, as well as differences in economic development among countries and the presence or absence of NHS. [ABSTRACT FROM AUTHOR]

This study aims to explore the distribution of visual attention on sustainability graphics when viewing an eco-friendly product and the recall of sustainability information afterward. Twenty-five students majoring in environmental studies and twenty-five students from non-environmental majors participated in the study. They were further divided into a higher group and a lower group based on their sustainability level. Participants viewed diagrams of an eco-trash boat design with sustainability graphics and a 15-page design description. Their eye-movement data and verbal reports on the recall of sustainability information were collected. Higher sustainability group had higher fixation count in sustainability graphics. Non-environmental majors had a shorter time to first fixation to sustainability graphics, and there was an interaction effect. Environmental students detected graphics faster in the lower group, but the opposite occurred in the higher group. Higher-sustainability non-environmental students were quicker, while the reverse was true for environmental students. In terms of recalling sustainability graphics, the higher group scored higher, while environmental majors scored higher in recalling sustainability features. In the recall coding, the most frequently mentioned terms were "green," "plant," "vivid," and "eco." The study offers new insights into sustainable development and provides design recommendations for eco-product designers. [ABSTRACT FROM AUTHOR]

Drawing a referent of a to-be-remembered word often results in better recognition and recall of this word relative to a control task in which the word is written, a pattern dubbed the drawing effect. Although this effect is not always found in pure lists, we report three experiments in which the drawing effect emerged in both pure- and mixed-lists on recognition and recall tests, though the effect was larger in mixed lists. Our experiments then compared drawing effects on memory between pure- and mixed-list contexts to determine whether the larger mixed-list drawing effect reflected a benefit to draw items, a cost to write items, or a combination. In delayed recognition and free-recall tests, a mixed-list benefit emerged for draw items in which memory for mixed-list draw items was greater than pure-list draw items. This mixed-list drawing benefit was accompanied by a mixed-list writing cost compared to pure-list write items, indicating that the mixed-list drawing effect does not operate cost-free. Our findings of a pure-list drawing effect are consistent with a memory strength account, however, the larger drawing effect in mixed lists suggest that participants may also deploy a distinctiveness heuristic to aid retrieval of drawn items. [ABSTRACT FROM AUTHOR]

Intrusion Detection Model (IDM) is an essential device for network defence in current trend. Malicious users analyse the vulnerabilities of IDSs to capture unauthorized access. Furthermore, intrusion detection encompasses numerous numerical attributes and models, resulting in elevated detection errors and triggering false alarms. Hence, optimal computational intelligence shall be incorporated in IDM to achieve high detection rate and less number of false alarms. Considering the same, a new hybrid IDM framework is developed as the combination of Fuzzy Genetic Algorithm with Multi-Objective Particle Swarm Optimization that maximizes the detection accuracy, minimizes the false alarms and takes less computational complexity which will be explained first phase. The existing IDSs are constraint to the information trained incur into false positives based on user continuity for normal activity. The objective of this proposal is to extract optimal classification rules automatically from training data that helps to identify types of attacks correctly including the unknown attack types. For achieving this goal, Multi-Objective Particle Swarm Optimization (MOPSO) is used as classifier to enhance the identification of the rare attack classes within the IDM. The effectiveness of this method lies in its capacity to leverage information within an unfamiliar search space, guiding subsequent searches towards valuable subspaces. It provides better separability of various classes' i.e. normal behaviour and false alarms. In this FGA-MOPSO model, Principal Component Analysis (PCA) serves as the feature selection technique employed to identify pertinent features within the dataset, thereby enhancing the classifier's performance and Fuzzy Genetic Algorithm (FGA) is used to create new population for training the classifier with the help of three operations namely selection, crossover and mutation that helps to practice more patterns in training phase and to obtain better understanding of the proposed classifier. The simulation will illustrate that the system is competent to speed-up the training and testing process of intrusions detection is important for network applications.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [Arun Kumar] Last name [Ramamoorthy]. Also, kindly confirm the details in the metadata are correct.Checked and Verified for Author 1. In Author 2 name, Given Name was [K.] and last name was[Karuppasamy], But its is just the opposite. Given Name is [Karuppasamy] and Last Name is [K.]. I have edited it. [ABSTRACT FROM AUTHOR]

The study aimed to classify two classes of vehicles, Tuktuk and Motorcycle, using a modified RCNN model. The MAjN_IRAQ Dataset, created from a camera system in Baghdad city, was used for training, detection, and classification of some vehicles to allow them to enter some crowded streets of Baghdad and to prevent others from entering the same streets. New layers were added and the number and size of filters were changed, which led to improve the process of training, detection and classification of vehicles with high accuracy, which leads to improving the proposed model's performance. The results showed that the modified RCNN model performed better when trained for 80 epochs. It improved performance measures such as precision, recall, and F1 score measure. The model was compared to other transfer learning methods (Alex Net, VGG16, and VGG19) and showed superior results for the Tuktuk class. The training and testing time for the proposed RCNNmodified model was also lower compared to the other models. At 80 epochs, the precision for the Tuktuk class was approximately 0.94, while for the Motorcycle class, it was approximately 0.89. The TPR was higher for the Tuktuk class at approximately 0.93, while the lower value was approximately 0.84 for VGG16. When the VGG16 model was used, the F1 score was better in the Motorcycle category (about 0.95) but worse in the Tuktuk category (0.86%). Both the suggested RCNN-modified model and the Alex Net model worked well in a reasonable amount of time. [ABSTRACT FROM AUTHOR]

IntroductionImplicit statistical learning is, by definition, learning that occurs without conscious awareness. However, measures that putatively assess implicit statistical learning often require explicit reflection, for example, deciding if a sequence is ‘grammatical’ or ‘ungrammatical’. By contrast, ‘processing-based’ tasks can measure learning without requiring conscious reflection, by measuring processes that are facilitated by implicit statistical learning. For example, when multiple stimuli consistently co-occur, it is efficient to ‘chunk’ them into a single cognitive unit, thus reducing working memory demands. Previous research has shown that when sequences of phonemes can be chunked into ‘words’, participants are better able to recall these sequences than random ones. Here, in two experiments, we investigated whether serial visual recall could be used to effectively measure the learning of a more complex artificial grammar that is designed to emulate the between-word relationships found in language.MethodsWe adapted the design of a previous Artificial Grammar Learning (AGL) study to use a visual serial recall task, as well as more traditional reflection-based grammaticality judgement and sequence completion tasks. After exposure to “grammatical” sequences of visual symbols generated by the artificial grammar, the participants were presented with novel testing sequences. After a brief pause, participants were asked to recall the sequence by clicking on the visual symbols on the screen in order.ResultsIn both experiments, we found no evidence of artificial grammar learning in the Visual Serial Recall task. However, we did replicate previously reported learning effects in the reflection-based measures.DiscussionIn light of the success of serial recall tasks in previous experiments, we discuss several methodological factors that influence the extent to which implicit statistical learning can be measured using these tasks.

Abstract Complex food supply chains face increasing risks that can affect product quality and safety, which can all lead to recalls. This paper analyzes the role that traceability plays in the different stages of beef recall management. A case study was conducted in two beef supply chains and stakeholders from these chains were interviewed. Content analysis was performed using QDA Miner software. The results are summarized into a framework that describes the role of traceability before, during, and after a recall. In total, 18 roles were identified, including reevaluating partnerships with suppliers. Some of these results had not previously been discussed in the literature. The findings indicate that both the target market segment and company size are key factors influencing the level of investment in traceability. Traceability is an increasingly essential aspect of the beef supply chain, yet empirical studies exploring this from various levels and stakeholder perspectives are limited. This study provides insights for professionals in the food supply chain helping them understand the various roles that food traceability plays in managing potential recalls.

Detection of heart diseases (HD) at an early stage diminishes the mortality rate. However, handling huge data is cumbersome for physicians. Hence, there is a need for a tool regarding the automation and processing of large data to help in making precise decisions. The proposed methodology aims to predict cases of HD or cardiovascular diseases (CD) well in advance based on the features of the patient. In the proposed methodology, the Cleveland Dataset of HD collected from University of California, Irvine (UCI) repository is evaluated. The various phases in the proposed methodology include, insight about the data, analyzing the data, feature engineering and finally building the model. The K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), classifier and convolutional neural network (CNN) are used to predict the HD and are evaluated on the dataset. The metrics considered are classification accuracy (CA), recall (r) and precision (p). The resulted CA, ‘r’ and ‘p’ for KNN-based approach are 66.7%, 91.7% and 88.8%, respectively. The SVM-based approach with ‘linear’ kernel achieved CA, ‘r’ and ‘p’ are 74.2%, 85.0% and 74.0%, respectively. The ANN-based approach resulted in 70.08%, 77.0% and 84.2% of CA, ‘p’ and ‘r’, respectively. Finally, the CNN-based prediction achieved CA, ‘p’ and ‘r’ are 83.61%, 76.0% and 97.0%, respectively. The experimental study concludes that the CNN-based prediction model outperformed the KNN, SVM and ANN-based prediction approaches regarding the prediction accuracy. An optimization algorithm can be incorporated into the model in future.

Virtual reality (VR) can place people in unique environments and facilitate engagement, making it a compelling tool for storytelling and learning. However, experiencing narratives requires immersion, which can be difficult for those who are anxious about technology. Prior research has shown that training new users on how to use VR before they engage in learning tasks housed in VR is critical. The right kind of training and targeted guidance may help people, including those with computer anxiety, better navigate virtual experiences. However, best practices for how training should be administered remain unclear. This study examined how training type (paper, video, and VR) and computer anxiety influenced outcomes using a large sample size (n = 284). We measured performance and self-reported outcomes while participants navigated computer-graphic scenes, manipulated three-dimensional objects, and watched a narrative 360° video. Results showed that participants who received training via video or VR mastered more VR functions than those who received training via paper. Additionally, those who trained directly in VR had less of a negative experience using VR for completing tasks. Furthermore, participants who trained in VR perceived the training as more useful and found the VR tasks to be easier compared to those who received training in paper or video. Finally, those with high levels of computer anxiety, regardless of training, had more negative outcomes than those with low computer anxiety, including having less mastery of VR functions and engagement with the 360° video content, perceiving the training as being less useful, completing tasks with more difficulty, and having more of a negative experience. Our results suggest that keeping the medium the same both during training and doing is ideal. We discuss implications for theories of information processing in VR, as well as implications for scaled engagement with narratives and learning in VR.

Copyright of China Textile Leader is the property of China Textile Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)